no code implementations • 26 Mar 2024 • Zhuoyuan Wu, Yuping Wang, Hengbo Ma, Zhaowei Li, Hang Qiu, Jiachen Li
Building on top of cooperative perception, this paper explores the feasibility and effectiveness of cooperative motion prediction.
no code implementations • 22 Jan 2024 • Jiachen Li, Chuanbo Hua, Hengbo Ma, Jinkyoo Park, Victoria Dax, Mykel J. Kochenderfer
In this paper, we propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
1 code implementation • 18 Dec 2023 • Zhixuan Liang, Yao Mu, Hengbo Ma, Masayoshi Tomizuka, Mingyu Ding, Ping Luo
Experiments on multi-task robotic manipulation benchmarks like Meta-World and LOReL demonstrate state-of-the-art performance and human-interpretable skill representations from SkillDiffuser.
no code implementations • 13 Oct 2022 • Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li, Joshua B. Tenenbaum, Chuang Gan
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics.
no code implementations • 10 Aug 2022 • Jiachen Li, Chuanbo Hua, Jinkyoo Park, Hengbo Ma, Victoria Dax, Mykel J. Kochenderfer
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited.
no code implementations • 5 Mar 2022 • Jiachen Li, Haiming Gang, Hengbo Ma, Masayoshi Tomizuka, Chiho Choi
We propose a novel approach for important object identification in egocentric driving scenarios with relational reasoning on the objects in the scene.
no code implementations • 4 Jan 2022 • Hengbo Ma, Bike Zhang, Masayoshi Tomizuka, Koushil Sreenath
By embedding the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture, we propose a differentiable safety-critical control framework that enables generalization to new environments for high relative-degree systems with forward invariance guarantees.
no code implementations • CVPR 2022 • Hengbo Ma, Jiachen Li, Ramtin Hosseini, Masayoshi Tomizuka, Chiho Choi
Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.
1 code implementation • 13 Sep 2021 • Zhao-Heng Yin, Lingfeng Sun, Hengbo Ma, Masayoshi Tomizuka, Wu-Jun Li
In this paper, we consider CDIL on a class of similar robots.
no code implementations • ICCV 2021 • Jiachen Li, Fan Yang, Hengbo Ma, Srikanth Malla, Masayoshi Tomizuka, Chiho Choi
Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.
no code implementations • 5 Jun 2021 • Defu Cao, Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain.
no code implementations • 18 Feb 2021 • Jiachen Li, Hengbo Ma, Zhihao Zhang, Jinning Li, Masayoshi Tomizuka
Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent.
no code implementations • 20 Aug 2020 • Liting Sun, Zheng Wu, Hengbo Ma, Masayoshi Tomizuka
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important.
no code implementations • 14 Feb 2020 • Jiachen Li, Hengbo Ma, Zhihao Zhang, Masayoshi Tomizuka
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios.
no code implementations • 5 May 2019 • Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control.
Ranked #14 on Trajectory Prediction on Stanford Drone
no code implementations • 2 May 2019 • Jiachen Li, Hengbo Ma, Wei Zhan, Masayoshi Tomizuka
In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task.
no code implementations • 4 Apr 2019 • Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite.
no code implementations • 9 Sep 2018 • Jiachen Li, Hengbo Ma, Wei Zhan, Masayoshi Tomizuka
Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making.